Systems and Methods for Occupant Classification

2. The occupant classification system of claim 1 wherein the posture classifier comprises a neural network, a support vector machine, a logistic regression, a decision tree, a Naïve-Bayes or nearest neighbors.

3. The occupant classification system of claim 1 wherein the deterministic component identifies the weight class based on the sum of the forces applied to each of the plurality of sensors.

4. The occupant classification system of claim 1 wherein the probabilistic component comprises a neural network, a support vector machine, a logistic regression, a decision tree, a Naïve-Bayes, nearest neighbors, regression-based models or a radial basis network.

7. The method of claim 6 wherein the probabilistic method comprises a neural network, a support vector machine, a logistic regression, a decision tree, a Naïve-Bayes or nearest neighbors.

8. The method of claim 6 wherein the deterministic method identifies the weight class based on a sum of the forces applied to each of the plurality of sensors.

9. The method of claim 6 wherein the second probabilistic method comprises a neural network, a support vector machine, a logistic regression, a decision tree, a Naïve-Bayes, nearest neighbors, regression-based models or a radial basis network.

12. The method of claim 11 wherein the probabilistic method comprises a neural network, a support vector machine, a logistic regression, a decision tree, a Naïve-Bayes, nearest neighbors, regression-based models or a radial basis network.

13. The method of claim 11 wherein the second probabilistic method comprises a neural network, a support vector machine, a logistic regression, a decision tree, a Naïve-Bayes, nearest neighbors, regression-based models or a radial basis network.

14. The method of claim 11, wherein the deterministic method identifies a weight class based on a sum of the forces applied to each of the plurality of sensors.